Lead Architect

It's fun to work in a company where people truly BELIEVE in what they are doing!

Role overview :

We’re building a next-gen LLMOps team at Fractal to industrialize GenAI implementation and shape the future of GenAI engineering. This is a hands-on technical leadership role for AI engineers with strong ML and DevOps skills — ideal for those who love building scalable systems from the ground up. You will be designing, deploying, and scaling GenAI and Agentic AI applications with robust lifecycle automation and observability.

Required Qualifications :

• 10 - 14 years of experience in working on ML projects that includes product building mindset, strong hands on skills, technical leadership, leading development teams
• Model development, training, deployment at scale, monitoring performance for production use cases
• Strong knowledge on Python, Data Engineering, FastAPI, NLP
• Knowledge on Langchain, Llamaindex, Langtrace, Langfuse, LLM evaluation, MLFlow, BentoML
• Should have worked on proprietary and open-source LLMs
• Experience on LLM fine tuning including PEFT / CPT
• Experience in creating Agentic AI workflows using frameworks like CrewAI, Langraph, AutoGen, Symantec Kernel
• Experience in performance optimization, RAG, guardrails, AI governance, prompt engineering, evaluation, and observability
• Experience in GenAI application deployment on cloud and on-premises at scale for production using DevOps practices
• Experience in DevOps and MLOps
• Good working knowledge on Kubernetes and Terraform
• Experience in minimum one cloud : AWS / GCP / Azure to deploy AI services
• Team player with excellent communication and presentation skills

Must have skills :

Product Thinking : Ideate, prototype, and scale internal accelerators and reusable components for LLMOps

GenAI Engineering : Productionize LLM-powered applications with modular, reusable, and secure patterns

Pipeline Architecture : Create evaluation pipelines — including prompt orchestration, feedback loops, and fine-tuning workflows

Prompt & Model Management : Design systems for versioning, AI governance, automated testing, and prompt quality scoring

Scalable Deployment : Architect cloud-native and hybrid deployment strategies for large-scale inference

Guardrails & Observability : Implement output filtering, context-aware routing, evaluation harnesses, metrics logging, and incident response

DevOps & Platform Automation : Drive end-to-end automation with Docker, Kubernetes, GitOps, Terraform, etc.

Must-Have Technical Skills

If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!

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